Visual Odometry (VO) and Visual SLAM (VSLAM) systems often struggle in low-light and dark environments due to the lack of robust visual features. In this paper, we propose a novel active illumination framework to enhance the performance of VO and V-SLAM algorithms in these challenging conditions. The developed approach dynamically controls a moving light source to illuminate highly textured areas, thereby improving feature extraction and tracking. Specifically, a detector block, which incorporates a deep learning-based enhancing network, identifies regions with relevant features. Then, a pan-tilt controller is responsible for guiding the light beam toward these areas, so that to provide information-rich images to the ego-motion estimation algorithm. Experimental results on a real robotic platform demonstrate the effectiveness of the proposed method, showing a reduction in the pose estimation error up to 75 % with respect to a traditional fixed lighting technique.

Autonomous robot navigation relies on the robot’s ability to understand its environment for localization, typically using a Visual Simultaneous Localization And Mapping (SLAM) algorithm that processes image sequences. While state-of-the-art methods have shown remarkable performance, they still have limitations. Geometric VO algorithms that leverage hand-crafted feature extractors require careful hyper-parameter tuning. Conversely, end-to-end data-driven VO algorithms suffer from limited generalization capabilities and require large datasets for their proper optimizations. Recently, promising results have been shown by hybrid approaches that integrate robust data-driven feature extraction with the geometric estimation pipeline. In this work, we follow these intuitions and propose a hybrid VO method, namely Learned Features For SLAM (LF2SLAM), that combines a deep neural network for feature extraction with a standard VO pipeline. The network is trained in a data-driven framework that includes a pose estimation component to learn feature extractors that are tailored for VO tasks. A novel loss function modification is introduced, using a binary mask that considers only the informative features. The experimental evaluation performed shows that our approach has remarkable generalization capabilities in scenarios that differ from those used for training. Furthermore, LF2SLAM exhibits robustness in more challenging scenarios, i.e., characterized by the presence of poor lighting and low amount of texture, with respect to the state-of-the-art ORB-SLAM3 algorithm.

Enabling Micro Aerial Vehicles (MAVs) with semi-autonomous capabilities to assist their teleoperation is crucial in several applications. Remote human operators do not have, in general, the situational awareness to perceive obstacles near the drone, nor the readiness to provide commands to avoid collisions. In this work, we devise a novel teleoperation setting that asks the operator to provide a simple high-level signal encoding the speed and the direction they expect the drone to follow. We then endow the MAV with an end-to-end Deep Reinforcement Learning (DRL) model that computes control commands to track the desired trajectory while performing collision avoidance. Differently from State-of-the-Art (SotA) works, it allows the robot to move freely in the 3D space, requires only the current RGB image captured by a monocular camera and the current robot position, and does not make any assumption about obstacle shape and size. We show the effectiveness and the generalization capabilities of our strategy by comparing it against a SotA baseline in photorealistic simulated environments.

Gaussian Processes (GPs) are expressive models for capturing signal statistics and expressing prediction uncer-tainty. As a result, the robotics community has gathered interest in leveraging these methods for inference, planning, and control. Unfortunately, despite providing a closed-form inference solution, GPs are non-parametric models that typically scale cubically with the dataset size, hence making them difficult to be used especially on onboard Size, Weight, and Power (SWaP) constrained aerial robots. In addition, the integration of popular libraries with GPs for different kernels is not trivial. In this paper, we propose GaPT, a novel toolkit that converts GPs to their state space form and performs regression in linear time. GaPT is designed to be highly compatible with several optimizers popular in robotics. We thoroughly validate the proposed approach for learning quadrotor dynamics on both single and multiple input GP settings. GaPT accurately captures the system behavior in multiple flight regimes and operating conditions, including those producing highly nonlin-ear effects such as aerodynamic forces and rotor interactions. Moreover, the results demonstrate the superior computational performance of GaPT compared to a classical GP inference approach on both single and multi-input settings especially when considering large number of data points, enabling real-time regression speed on embedded platforms used on SWaP-constrained aerial robots.

Abstract The performances of aircraft braking control systems are strongly influenced by the tire friction force experienced during the braking phase. The availability of an accurate estimate of the current airstrip characteristics is a recognized issue for developing optimized braking control schemes. The study presented in this paper is focused on the robust online estimation of the airstrip characteristics from sensory data usually available on an aircraft. In order to capture the nonlinear dependency of the current best slip on sequential slip-friction measurements acquired during the braking maneuver, multilayer perceptron (MLP) approximators have been proposed. The MLP training is based on a synthetic data set derived from a widely used tire–road friction model. In order to achieve robust predictions, MLP architectures based on the drop-out mechanism have been applied not only in the offline training phase but also during the braking. This allowed to online compute a confidence interval measure for best friction estimate that has been exploited to refine the estimation via Kalman Filtering. Open loop and closed loop simulation studies in 15 representative airstrip scenarios (with multiple surface transitions) have been performed to evaluate the performance of the proposed robust estimation method in terms of estimation error, aircraft braking distance, and time, together with a quantitative comparison with a state-of-the-art benchmark approach.